Un modèle d’enseignement axé sur les stratégies d’écriture et adapté pour favoriser une pratique culturellement durable
Bibliographic record
Abstract
According to programs of study for the teaching of French in Canada, students must learn to write various genres of texts, thinking about their intention and mobilizing various writing strategies. This objective poses a significant challenge for students from the Francophone linguistic minority community. Moreover, certain current teaching practices tend to reinforce the linguistic insecurity of these students, impede the development of their ability to write, marginalizing minoritized students by favouring dominant repertoires. This observation leads us to rethink our practices to promote written discourse that develops students’ confidence in their skills as writers. The article presents a teaching model that devotes significant attention to writing strategies and that has been adapted to better respond to the needs of a diverse population. The choice of these components draws on research conducted in the following fields: editorial expertise from a cognitive psychology perspective, discourse analysis, social constructivism, teaching practices and culturally sustainable pedagogy. The article ends with practical recommendations for teachers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".